Face Detection using Variance based Haar-Like feature and SVM

This paper proposes a new approach to perform the problem of real-time face detection. The proposed method combines primitive Haar-Like feature and variance value to construct a new feature, so-called Variance based Haar-Like feature. Face in image can be represented with a small quantity of features using this new feature. We used SVM instead of AdaBoost for training and classification. We made a database containing 5,000 face samples and 10,000 non-face samples extracted from real images for learning purposed. The 5,000 face samples contain many images which have many differences of light conditions. And experiments showed that face detection system using Variance based Haar-Like feature and SVM can be much more efficient than face detection system using primitive Haar-Like feature and AdaBoost. We tested our method on two Face databases and one Non-Face database. We have obtained 96.17% of correct detection rate on YaleB face database, which is higher 4.21% than that of using primitive Haar-Like feature and AdaBoost.

Authors:

• Cuong Nguyen Khac
• Ju H. Park
• Ho-Youl Jung

Keywords:

• AdaBoost
• Haar-Like feature
• SVM
• variance
• Variance based Haar-Like feature.

References:

[1] Tan. H, Chen. H, Selecting Frequency Feature for License Plate Detection
Based on AdaBoost, Proceedings of SPIE-IS and T Electronic Imaging-
Visual Communications and Image Processing
[2] Negri. P, Clady. X, Prevost. L, Benchmarking Haar and Histograms of
Oriented Gradients features applied to vehicle detection, Proceedings of
the Fourth International Conference on Informatics in Control, Automation
and Robotics, 2007
[3] Bai. H, Wu. J, Liu. C, Motion and haar-like features based vehicle
detection, Multi-Media Modelling Conference Proceedings, 2006 12th
International
[4] Stanciulescu. B, Breheret. A, Moutarde. F, Introducing New AdaBoost
Features for Real-Time Vehicle Detection, Proceedings of COGIS-07
conference on COGnitive systems with Interactive Sensors, held in
Stanford University California, Nov 2007
[5] Haselhoff. A, Kummert. A, Schneider. G, Radar-Vision Fusion with an
Application to Car-Following using an Improved AdaBoost Detection
Algorithm, Intelligent Transportation Systems Conference, ITSC 2007,
IEEE, pages: 854-858, Sept 2007
[6] Lausser. L, Schwenker. F, Palm. G, Detecting zebra crossings utilizing
AdaBoost, 16th European Symposium on Artificial Neural Networks,
Bruges, Belgium, April 23-25, 2008
[7] Yoon. C, Cheon. M, Kim. E, Park. M, Lee. H, Real-time road sign detection
using Adaboost and Multicandidate, 2007 International Symposium
on Advanced Intelligent Systems, Sokcho, Korea
[8] Nishimura. J, Kuroda. T, Low cost speech detection using Haar-like filtering
for sensornet, Signal Processing, 2008. ICSP 2008. 9th International
Conference, pages: 2608-2611, Oct. 2008
[9] Viola. P, Jones. M, Rapid object detection using a boosted cascade
of simple features, Proceedings of the 2001 IEEE Computer Society
Conference, vol 1, pages: 511-518, 2001
[10] Viola. P, Jones. M, Robust Real-time Object Detection, Second international
workshop on statistical and computational theories of vision -
modeling, learning, computing, and sampling. Vancouver, Canada, July
13, 2001
[11] Viola. P, Jones. M, Robust real-time face detection, International Journal
of Computer Vision 57(2), 137-154, 2004, vol 2, pages: 747-747, July
2001
[12] Paisitkriangkrai. S, Shen. C, Zhang. J, Fast Pedestrian Detection Using a
Cascade of Boosted Covariance Features, Circuits and Systems for Video
Technology, IEEE Transactions, vol 18, pages: 1140-1151, Aug 2008
[13] Feng Tang, Crabb. R, Hai Tao, Representing Images Using Nonorthogonal
Haar-Like Bases, Pattern Analysis and Machine Intelligence, IEEE
Transactions, vol 29, pages: 2120-2134, Dec. 2007
[14] Chen. Q, Georganas. N. D, Petriu. E. M, Hand Gesture Recognition
Using Haar-Like Features and a Stochastic Context-Free Grammar,
Instrumentation and Measurement, IEEE Transactions, vol 57, pages:
1562-1571, Aug 2008
[15] Sonka. M,Hlavac. V, Boyle. R, Image Processing, Analysis, and Machine
Vision. Boston, MA: PWS-Kent, 1999.
[16] Andrew Webb, Statistical Pattern Recognition, Oxford University Press,
New York, 1999.